Classification and Visualization

Documentation for package ‘klaR’ version 1.7-3

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-- B --

b.scal	Calculation of beta scaling parameters
B3	West German Business Cycles 1955-1994
benchB3	Benchmarking on B3 data
betascale	Scale membership values according to a beta scaling

-- C --

calc.trans	Calculation of transition probabilities
centerlines	Lines from classborders to the center
classscatter	Classification scatterplot matrix
cond.index	Calculation of Condition Indices for Linear Regression
corclust	Function to identify groups of highly correlated variables for removing correlated features from the data for further analysis.
countries	Socioeconomic data for the most populous countries.
cvtree	Extracts variable cluster IDs

-- D --

dkernel	Estimate density of a given kernel
drawparti	Plotting the 2-d partitions of classification methods

-- E --

e.scal	Function to calculate e- or softmax scaled membership values
EDAM	Computation of an Eight Direction Arranged Map
errormatrix	Tabulation of prediction errors by classes

-- F --

friedman.data

Friedman's classification benchmark data

-- G --

GermanCredit	Statlog German Credit
greedy.wilks	Stepwise forward variable selection for classification
greedy.wilks.default	Stepwise forward variable selection for classification
greedy.wilks.formula	Stepwise forward variable selection for classification

-- H --

hmm.sop

Calculation of HMM Sum of Path

-- K --

kmodes

K-Modes Clustering

-- L --

level_shardsplot	Plotting Eight Direction Arranged Maps or Self-Organizing Maps
loclda	Localized Linear Discriminant Analysis (LocLDA)
loclda.data.frame	Localized Linear Discriminant Analysis (LocLDA)
loclda.default	Localized Linear Discriminant Analysis (LocLDA)
loclda.formula	Localized Linear Discriminant Analysis (LocLDA)
loclda.matrix	Localized Linear Discriminant Analysis (LocLDA)
locpvs	Pairwise variable selection for classification in local models

-- M --

meclight	Minimal Error Classification
meclight.data.frame	Minimal Error Classification
meclight.default	Minimal Error Classification
meclight.formula	Minimal Error Classification
meclight.matrix	Minimal Error Classification

-- N --

NaiveBayes	Naive Bayes Classifier
NaiveBayes.default	Naive Bayes Classifier
NaiveBayes.formula	Naive Bayes Classifier
nm	Nearest Mean Classification
nm.data.frame	Nearest Mean Classification
nm.default	Nearest Mean Classification
nm.formula	Nearest Mean Classification
nm.matrix	Nearest Mean Classification

-- P --

partimat	Plotting the 2-d partitions of classification methods
partimat.data.frame	Plotting the 2-d partitions of classification methods
partimat.default	Plotting the 2-d partitions of classification methods
partimat.formula	Plotting the 2-d partitions of classification methods
partimat.matrix	Plotting the 2-d partitions of classification methods
plineplot	Plotting marginal posterior class probabilities
plot.corclust	Function to identify groups of highly correlated variables for removing correlated features from the data for further analysis.
plot.EDAM	Plotting Eight Direction Arranged Maps or Self-Organizing Maps
plot.NaiveBayes	Naive Bayes Plot
plot.rda	Regularized Discriminant Analysis (RDA)
plot.stepclass	Stepwise variable selection for classification
plot.woe	Plot information values
predict.loclda	Localized Linear Discriminant Analysis (LocLDA)
predict.locpvs	predict method for locpvs objects
predict.meclight	Prediction of Minimal Error Classification
predict.NaiveBayes	Naive Bayes Classifier
predict.pvs	predict method for pvs objects
predict.rda	Regularized Discriminant Analysis (RDA)
predict.sknn	Simple k Nearest Neighbours Classification
predict.svmlight	Interface to SVMlight
predict.woe	Weights of evidence
print.greedy.wilks	Stepwise forward variable selection for classification
print.kmodes	K-Modes Clustering
print.loclda	Localized Linear Discriminant Analysis (LocLDA)
print.meclight	Minimal Error Classification
print.pvs	Pairwise variable selection for classification
print.rda	Regularized Discriminant Analysis (RDA)
print.stepclass	Stepwise variable selection for classification
print.woe	Weights of evidence
pvs	Pairwise variable selection for classification
pvs.default	Pairwise variable selection for classification
pvs.formula	Pairwise variable selection for classification

-- Q --

quadplot

Plotting of 4 dimensional membership representation simplex

-- R --

rda	Regularized Discriminant Analysis (RDA)
rda.default	Regularized Discriminant Analysis (RDA)
rda.formula	Regularized Discriminant Analysis (RDA)

-- S --

shardsplot	Plotting Eight Direction Arranged Maps or Self-Organizing Maps
sknn	Simple k nearest Neighbours
sknn.data.frame	Simple k nearest Neighbours
sknn.default	Simple k nearest Neighbours
sknn.formula	Simple k nearest Neighbours
sknn.matrix	Simple k nearest Neighbours
stepclass	Stepwise variable selection for classification
stepclass.default	Stepwise variable selection for classification
stepclass.formula	Stepwise variable selection for classification
svmlight	Interface to SVMlight
svmlight.data.frame	Interface to SVMlight
svmlight.default	Interface to SVMlight
svmlight.formula	Interface to SVMlight
svmlight.matrix	Interface to SVMlight

-- T --

triframe	Barycentric plots
trigrid	Barycentric plots
trilines	Barycentric plots
triperplines	Barycentric plots
triplot	Barycentric plots
tripoints	Barycentric plots
tritrafo	Barycentric plots

-- U --

ucpm	Uschi's classification performance measures

-- W --

woe	Weights of evidence
woe.default	Weights of evidence
woe.formula	Weights of evidence

-- X --

xtractvars

Variable clustering based variable selection